Nuclear DNA-Gated Electrochemiluminescence Microscopy with Deep Learning for Single-Cell Apoptosis Profiling

Area of Science:

• Cell biology
• Biochemistry
• Microscopy

Background:

• Assessing cellular responses to anticancer drugs is difficult due to limitations in current assays.
• Existing methods often provide bulk or binary readouts, hindering quantitative single-cell analysis.

Purpose of the Study:

• To develop a scalable, quantitative method for single-cell apoptosis profiling.
• To enable mechanism-relevant quantification of cellular responses to anticancer agents.

Main Methods:

• Development of a nuclear DNA-gated electrochemiluminescence (ECL) microscopy assay.
• Utilizing genomic DNA as a coreactant for Ru(bpy)3^2+ to detect chromatin changes.
• Employing a deep learning model for high-throughput classification of apoptotic cells from ECL images.

Main Results:

• The ECL signal quantitatively measures apoptotic progression in a dose- and time-dependent manner.
• The assay successfully profiles apoptosis across large datasets, enabling comparison of drug potencies.
• Distinct spatial damage signatures were identified for photosensitizers, correlating with their ROS-mediated pathways.

Conclusions:

• The developed ECL microscopy assay provides a quantitative and scalable strategy for single-cell apoptosis profiling.
• This approach overcomes limitations of traditional assays, offering mechanism-relevant insights into anticancer agent efficacy.